DBMS > Amazon Redshift vs. Datomic vs. Ehcache vs. IBM Db2 Event Store vs. RethinkDB

System Properties Comparison Amazon Redshift vs. Datomic vs. Ehcache vs. IBM Db2 Event Store vs. RethinkDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

A widely adopted Java cache with tiered storage options

Distributed Event Store optimized for Internet of Things use cases

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

Primary database model

Relational DBMS

Key-value store

Event Store
Time Series DBMS

Document store

Secondary database models

Spatial DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	4.89
Rank	#67	Overall
	#8	Key-value stores

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	2.74
Rank	#105	Overall
	#19	Document stores

Website

aws.amazon.com/redshift

www.datomic.com

www.ehcache.org

www.ibm.com/products/db2-event-store

rethinkdb.com

Technical documentation

docs.aws.amazon.com/redshift

docs.datomic.com

www.ehcache.org/documentation

www.ibm.com/docs/en/db2-event-store

rethinkdb.com/docs

Developer

Amazon (based on PostgreSQL)

Cognitect

Terracotta Inc, owned by Software AG

IBM

The Linux Foundation

since July 2017

Initial release

2012

2009

2017

2009

Current release

1.0.6735, June 2023

3.10.0, March 2022

2.0

2.4.1, August 2020

License

Commercial or Open Source

commercial

limited edition free

Open Source

Apache Version 2; commercial licenses available

commercial

free developer edition available

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, Clojure

Java

C and C++

C++

Server operating systems

hosted

All OS with a Java VM

Linux

Linux, macOS, Windows for the developer addition

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

string, binary, float, bool, date, geometry

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

through the embedded Spark runtime

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

JCache

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
Java

Java

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Transaction Functions

yes

Triggers

By using transaction functions

yes

Cache Event Listeners

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

But extensive use of caching in the application peers

Sharding

by using Terracotta Server

Sharding

range based

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

But extensive use of caching in the application peers

yes

by using Terracotta Server

Active-active shard replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Tunable Consistency (Strong, Eventual, Weak)

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

yes

supports JTA and can work as an XA resource

Atomic single-document operations

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

MVCC based

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

using a tiered cache-storage approach

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

yes

User concepts

Access control

fine grained access rights according to SQL-standard

yes

users and table-level permissions

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